Our goal is to help interpret AI algorithms to acquire, process, and understand visual data.
Artificial Intelligence (AI)
All About AI
Artificial intelligence relies on a technique called machine learning, where computers are trained to understand data. So, for example, if you want a computer to be able to recognize images, you might feed it thousands of pictures and tell it what each image shows.
From healthcare to business operations, AI has the potential to help people achieve more. AI can be used to analyze data, understand images and sounds, forecast outcomes, and translate languages.
AI leverages machine learning, big data analytics, natural language processing, and robotic process automation to automate repetitive and complex tasks.
Experts believe that AI will continue to play an increasingly important role in our future.
Machine Learning (ML)
AI vs. ML
Machine learning and artificial intelligence are closely related fields, but they are not the same thing.
Machine learning is one type of artificial intelligence, meaning a computer is capable of human-like tasks.
AI is a broader concept that refers to any computer program capable of human-like tasks, including machine learning.
Machine learning is a technology that gives computers the ability to learn without being explicitly programmed.
In other words, the computer can figure things out based on examples we give it.
Machine learning is used in everything from self-driving cars to the health care industry. Major tech companies, like Amazon, Google, and Microsoft, rely on machine learning to improve their products and services.
The autonomous vehicles industry rises by 16% globally every year.
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Computer Vision (CV): The Next Gen
What is Computer Vision
Computer vision is the science that allows machines to "see".
The most common application of computer vision is in photo or video editing software, but the technology is also used in many other fields, such as robotics, security systems and medical imaging.
A computer vision system needs to be trained to recognize different objects or items in an image, depending on the task.
For example, a vision system that can detect if a truck is in a street-level image would need to recognize a truck and be able to identify its exact location on the road.
If you want to learn more about the Overlab's speciality, computer vision, then click the button below!
